Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.
Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases.
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转化医学数据的整合和可视化，以更好地理解人类疾病。
DOI：
10.1089/big.2015.0057
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发表时间：
2016-06
期刊：
影响因子：
4.6
通讯作者：
Schneider R
中科院分区：
文献类型：
--
作者：
Satagopam V;Gu W;Eifes S;Gawron P;Ostaszewski M;Gebel S;Barbosa-Silva A;Balling R;Schneider R
转化医学是将基础生命科学研究成果转化为临床环境中的新工具和方法的领域，例如，作为新的诊断或治疗方法。如今，翻译的过程是由大量的异构数据，从医疗数据到整个范围的组学数据的支持。这不仅是一个巨大的机遇，也是一个巨大的挑战，因为转化医学大数据难以整合和分析，需要生物医学专家参与数据处理。我们在这里展示了可视化和可互操作的工作流程，结合了多个复杂的步骤，至少可以解决部分挑战。在这篇文章中，我们提出了一个综合的工作流程，用于在人类健康的背景下探索，分析和解释转化医学数据。三个Web服务-transSMART、Galaxy Server和MINERVA平台-被合并到一个大数据管道中。原生可视化功能使生物医学专家能够全面了解和控制工作流程的各个步骤。transSMART的功能可以灵活地过滤多维集成数据集，以创建适合下游处理的子集。Galaxy服务器通过使用现有或自定义组件，提供分析管道的视觉辅助构建。MINERVA平台支持在情境化分析可视化系统中探索健康和疾病相关机制。我们通过使用现有数据集说明其后续步骤来演示我们的工作流程的实用性，为此我们提出了过滤方案，分析管道和相应的分析结果可视化。该工作流作为沙箱环境提供，读者可以自己使用所描述的设置。总的来说，我们的工作显示了大数据处理服务的可视化和接口如何促进转化医学数据的探索，分析和解释。
Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services—tranSMART, a Galaxy Server, and a MINERVA platform—are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.